Ambiguity-Restrained Text-Video Representation Learning for Partially Relevant Video Retrieval
CH Cho, WJ Moon, W Jun, MS Jung, JP Heo·June 09, 2025
Summary
The paper introduces ARL, a method for Partially Relevant Video Retrieval, addressing ambiguity between text and video content. It uses a framework with uncertainty and similarity criteria, multi-positive contrastive learning, and dual triplet margin loss to learn semantic relationships. Cross-model ambiguity detection reduces error propagation, enhancing PRVR effectiveness. ARL surpasses existing methods like MS-SL and GMMFormer.
Introduction
Background
Overview of video retrieval challenges
Importance of relevance in video content
Current limitations in Partially Relevant Video Retrieval (PRVR)
Objective
To introduce ARL, a novel method for PRVR
To address ambiguity between text and video content
To enhance the effectiveness of PRVR through a comprehensive framework
Method
Framework Components
Uncertainty and similarity criteria
Multi-positive contrastive learning
Dual triplet margin loss
Learning Semantic Relationships
Utilization of deep learning techniques
Capturing complex relationships between text and video
Cross-model Ambiguity Detection
Reducing error propagation in PRVR
Enhancing the robustness of the retrieval process
Evaluation
Comparison with Existing Methods
MS-SL (Multi-Scale Learning)
GMMFormer (Generalized Multi-modal Transformer)
Metrics and Results
Quantitative analysis of performance
Qualitative insights into the effectiveness of ARL
Conclusion
Summary of Contributions
Key innovations in ARL
Future Work
Potential extensions and improvements
Impact and Applications
Real-world implications of ARL in video retrieval
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What specific loss functions are utilized within the ARL framework, and what is their purpose?
Against which existing methods was ARL compared, and what were the results of the comparison?
What are the key components and techniques used in the ARL method for Partially Relevant Video Retrieval?
How does ARL address the ambiguity between text and video content in Partially Relevant Video Retrieval?